Project 2: Multi-Omics of high-risk MM

项目概要/摘要 虽然我们自己的广泛研究已经证实了被破坏的基因组在多种疾病中的重要作用 骨髓瘤（MM），他们还重新强调了理解上的差距以及免疫的重要性 调控和基因-环境相互作用。 MM 的进化确实很可能是在之前或期间 治疗，是由遗传变化驱动的生物扰动复杂相互作用的结果 环境影响。我们过去的工作还表明，对少数患者进行研究 对于科学理解来说，深入的研究与对表面基因组事件的研究一样有益。 数千名患者。因此，我们将努力开展第一个纵向转化临床试验并 最高风险 MM 人群环境遗传相互作用的综合数据资源。它是 这些患者由于仍然完全无法接受高效治疗而继续迅速失败 不透明。需要采用最先进技术的新的、大胆的方法来扭转数十年之久的局面 缺乏进步。 我们的假设是，对高分辨率捕获基因-环境相互作用的数据进行分析将揭示 深入了解影响 MM 对治疗反应和后续结果的生物学途径。首先， 我们将利用经过仔细研究和同质治疗的高风险“双重打击”患者群体 具有大型对照临床数据库和生物存储库的 2 期临床试验，为每位患者得出一个 随着时间的推移与临床结果相关的环境基因相互作用的详细图谱。其次，我们将执行一个 一系列复杂的分析，以确定基因组、转录组中和跨基因组、转录组、 表观基因组、免疫环境、蛋白质组、脂质组和代谢组。第三，我们将研究这些样本 当今技术上可行的最高分辨率，并寻求定义基因与环境相互作用的变化 随着时间的推移，这与对治疗的反应相关。最后，高分辨率数据捕获这些相互作用 变化和临床反应数据将联系起来，以提高我们对潜在机制的理解 MM 患者在疾病结果方面的变异性。这一综合资源将使 MM 临床监测和治疗的更个性化方法。

PROJECT SUMMARY/ABSTRACT While our own extensive studies have confirmed the significant role of the disrupted genome in Multiple Myeloma (MM), they have also re-emphasized the gaps in understanding, and the importance of immune regulation and gene-environment interaction. It is indeed likely that the evolution of MM, both before or during therapy, is the result of a complex interplay of biological perturbations driven by genetic changes and environmental influences. Our past work has also demonstrated that studying small numbers of patients at great depth can be as rewarding for scientific understanding as studies of superficial genomic events in thousands of patients. Thus we will strive to generate the first, longitudinal, translational clinical trial and comprehensive data resource of environmental genetic interactions for the highest-risk MM population. It is these patients who continue to rapidly fail highly effective therapeutics for reasons which are still completely opaque. New and bold approaches using state-of-the-art technology are required to reverse this decades-old lack of progress. Our hypothesis is that analysis of data capturing gene-environment interactions at high resolution will reveal insights into biological pathways influencing MM responsiveness to therapy and subsequent outcomes. First, we will leverage a carefully studied and homogeneously treated high-risk group of “double hit” patients in a Phase 2 clinical trial with large control clinical databases and bio-repositories to derive, for each patient, a detailed map of environmental gene interactions linked to clinical outcome over time. Second, we will perform a series of complex analyses to identify MM-associated changes in and across the genome, transcriptome, epigenome, immune environment, proteome, lipidome and metabolome. Third, we will study these samples at the highest resolution technically feasible today, and seek to define gene-environment interaction changes over time that associate with response to therapy. Finally, high resolution data capturing these interaction changes and clinical response data will be linked to improve our understanding of the mechanisms underlying MM variability among patients in regards to disease outcomes. This comprehensive resource will enable a more individualized approach to clinical surveillance and therapy for MM.